Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.
First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.
```r
#install.packages("gganimate")
#install.packages("gifski")
#install.packages("av")
#install.packages("gapminder")
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.4 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 2.0.1 ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(gganimate)
library(gifski)
library(av)
library(gapminder)
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
Let’s plot all the countries in 1952.
theme_set(theme_bw()) # set theme to white background for better visibility
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
…
We see an interesting spread with an outlier to the right. Answer the following questions, please:
gapminder %>% #chosing dataset gapminder
filter(year == 1952) %>% #Take every data in the dataset from the year 1952
select(country, gdpPercap) %>% #select data within the coloumn country and gdpPercap in the year 1952
arrange(desc(gdpPercap)) #Arrange the countries with gdpPercap in descending order
Next, you can generate a similar plot for 2007 and compare the differences
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
…
The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.
Tasks:
options(scipen = 999) #changes the scientific notations to whole numbers
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop, color = continent)) + #creating a ggplot the only looks at the year 2007, and has gdpPercap on the x-axis, lifeExp on the y-axis. The plot also changes the size of the dots by population and the color by continent
geom_point() + #creating a scatterplot
scale_x_log10() + #scales down the big difference in numbers in gdp
labs(y= "Life expectancy", x = "GDP pr. capita") #creates new names on the y and x axis
gapminder %>%
filter(year == 2007) %>% #filtering for the year 2007
select(country, gdpPercap) %>% #selecting country and gdpPercap as data output
arrange(desc(gdpPercap)) %>% #arranges gdpPercap in descending order
head(5) #makes the output take only the top 5 from the descending order
The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. Beware that there may be other packages your operating system needs in order to glue interim images into an animation or video. Read the messages when installing the package.
Also, there are two ways of animating the gapminder ggplot.
The first step is to create the object-to-be-animated
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() # convert x to log scale
anim
…
This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the bottom right ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the visual inside an html file.
anim + transition_states(year,
transition_length = 1,
state_length = 1)
…
Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.
This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
transition_time(year)
anim2
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
Now, choose one of the animation options and get it to work. You may need to troubleshoot your installation of gganimate and other packages
transition_states() and transition_time() functions respectively)anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
labs(title = 'Year: {frame_time}', x = 'gdpPercap', y = 'lifeExp') +
transition_time(year) #makes an animated plot with a title to follow. It shows different periods of time with different gdpPercap and lifeEp for each country
anim2
anim3 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point() +
scale_x_log10() + # convert x to log scale
transition_time(year) +
labs( title = "year:{frame_time}", x = "GDP per Capita", y = "Life Expectancy") #couldn't make this work
anim3
gapminder_unfiltered dataset and download more at https://www.gapminder.org/data/ ]#Find the top 10 most populated Asien countries in year 2000 and figure out which three countries has the lowest gdpPercap and insert into a barchart
gapminder %>%
filter(continent == "Asia", year == 2000) %>% #filters the asien countries in year 2000
select(country, gdpPercap) %>% #selects country and gdpPercap as data output
arrange(order(gdpPercap)) %>% #arranges in asscending order, since the order() has asscending by default
head(3) %>% #only takes the top three countries.
ggplot(aes(x= country, y = gdpPercap, fill = country)) + geom_bar(stat = "identity")